Extracting planes from sparse 3D LiDAR data with measurement noise model

Abstract. 3D LiDAR sensors play an important part in several autonomous navigation and perception systems with the technology evolving rapidly over time. This work presents the preliminary evaluation results of a 3D solid state LiDAR sensor. Different aspects of this new type of sensor are studied and their data are characterized for their effective utilization for object detection for the application of Autonomous Ground Vehicles (AGV). The paper provides a set of evaluations to analyze the characterizations and performances of such LiDAR sensors. After characterization of the sensor, the performance is also evaluated in real environment with the sensors mounted on top of a vehicle and used to detect and classify different objects using a state-of-the-art Super-Voxel based method. The 3D point cloud obtained from the sensor is classified into three main object classes “Building”, “Ground” and “Obstacles”. The results evaluated on real data, clearly demonstrate the applicability and suitability of the sensor for such type of applications.

3D point cloud-based place recognition has gotten more attention since 3D LiDAR sensors are widely used for robotic applications and autonomous driving. Most of the existing deep point cloud-based methods take a few regular number points or image-like formats as inputs which are inability to make full use of point clouds' geometric information. This paper proposes a novel place recognition approach that is flexible and effective to handle diverse numbers of 3D LiDAR point clouds in large-scale environments. The approach is composed of feature extraction and a global descriptor encoding. The feature extraction consumes the 3D LiDAR point cloud with KPConv that can extract features efficiently and flexibly. Before the global descriptor encoding, a transformer module is employed to aggregate the contextual information that exploits the relationship of all features. The NetVLAD layer encodes the features into a global descriptor for recognizing a similar place rapidly. The proposed approach is evaluated on the KITTI odometry dataset, which demonstrates the validity of the proposed approach.

LiDAR odometry algorithms are extensively studied for vehicular positioning. However, achieving high-precision positioning using low-cost 16-channel LiDAR in urban canyons remains a challenging problem due to the limited point cloud density from low-cost LiDAR and excessive dynamic surrounding objects. To fill this gap, this paper proposes enriching sparse 3D point clouds to denser clouds via a novel deep learning-based superresolution (SR) algorithm before its utilization in 3D LiDAR odometry. We validate the effectiveness of the proposed method using the KITTI dataset and a challenging dataset collected in an urban canyon (with complex environmental structures and dynamic objects) of Hong Kong. We conclude that significantly denser point clouds are achieved with considerable accuracy. In addition, significantly improved performance of 3D LiDAR odometry is obtained in the evaluated dataset collected in an urban canyon of Hong Kong.

Navigation system for orchard spraying robot based on 3D LiDAR SLAM with NDT_ICP point cloud registration

Autonomous aerial refueling (AAR) is an important capability for an unmanned aerial vehicle (UAV) to increase its flying range and endurance without increasing its size. This paper presents a novel tracking method that utilizes both 2D intensity and 3D point-cloud data acquired with a 3D Flash LIDAR sensor to establish relative position and orientation between the receiver vehicle and drogue during an aerial refueling process. Unlike classic, vision-based sensors, a 3D Flash LIDAR sensor can provide 3D point-cloud data in real time without motion blur, in the day or night, and is capable of imaging through fog and clouds. The proposed method segments out the drogue through 2D analysis and estimates the center of the drogue from 3D point-cloud data for flight trajectory determination. A level-set front propagation routine is first employed to identify the target of interest and establish its silhouette information. Sufficient domain knowledge, such as the size of the drogue and the expected operable distance, is integrated into our approach to quickly eliminate unlikely target candidates. A statistical analysis along with a random sample consensus (RANSAC) is performed on the target to reduce noise and estimate the center of the drogue after all 3D points on the drogue are identified. The estimated center and drogue silhouette serve as the seed points to efficiently locate the target in the next frame.

In recent years, due to the significant advancements in hardware sensors and software technologies, 3D environmental point cloud modeling has gradually been applied in the automation industry, autonomous vehicles, and construction engineering. With the high-precision measurements of 3D LiDAR, its point clouds can clearly reflect the geometric structure and features of the environment, thus enabling the creation of high-density 3D environmental point cloud models. However, due to the enormous quantity of high-density 3D point clouds, storing and processing these 3D data requires a considerable amount of memory and computing time. In light of this, this paper proposes a real-time 3D point cloud environmental contour modeling technique. The study uses the point cloud distribution from the 3D LiDAR body frame point cloud to establish structured edge features, thereby creating a 3D environmental contour point cloud map. Additionally, unstable objects such as vehicles will appear during the mapping process; these specific objects will be regarded as not part of the stable environmental model in this study. To address this issue, the study will further remove these objects from the 3D point cloud through image recognition and LiDAR heterogeneous matching, resulting in a higher quality 3D environmental contour point cloud map. This 3D environmental contour point cloud not only retains the recognizability of the environmental structure but also solves the problems of massive data storage and processing. Moreover, the method proposed in this study can achieve real-time realization without requiring the 3D point cloud to be organized in a structured order, making it applicable to unorganized 3D point cloud LiDAR sensors. Finally, the feasibility of the proposed method in practical applications is also verified through actual experimental data.

This paper focuses on the train under-vehicle inspection robot equipped with 3D solid-state LiDAR and laser distance sensor. Dedicated 3D Simultaneous Localization and Mapping (SLAM) framework and motion control algorithm are proposed for the robot operating in the hazardous environment of the inspection pit. The constrained working environment includes repetitive and monotonous spatial features makes autonomous localization and navigation task a challenging issue. The proposed SLAM localization framework has a complete architecture that includes frontend laser odometry and loop closure detection. The method optimized for 3D solid-state LiDAR is utilized for feature extraction. The pose optimization problem is initialized with the motion estimation provided by wheel odometry. The wheel odometry interpolation is utilized to increase the pose update frequency. Moreover, the features of the vehicle wheels in the measurements of laser distance sensor are utilized for loop closure detection. The proposed motion control algorithm extracts the side features of the inspection pit for feedback of angular velocity closed-loop control. The experimental results from simulation and real-world show that the robot can be driven accurately along a straight trajectory in the inspection pit with a maximum integrated localization and navigation error of less than 0.015m in movement over 200m, which demonstrates the effectiveness of the proposed algorithms.

Abstract. Utilizing ground control points (GCPs) to georeference photogrammetry-based point cloud data is a common practice in unmanned aerial system (UAS) mapping. Direct georeferencing or integrated sensor orientation (ISO) can be used to obtain georeferenced point clouds from UAS without relying heavily on GCPs. However, the accuracy of the point cloud may be impacted by the accuracy of the trajectory solution obtained by GNSS. To improve point cloud accuracy, post-processing kinematic (PPK) solutions can be applied to the UAS trajectory, which may provide higher accuracy than low-accuracy trajectory solutions and minimize the reliance on GCPs. This study compares the accuracy and precision of two different point clouds generated using different methods. One point cloud was generated using traditional photogrammetric methods with low accuracy Global Navigation Satellite System (GNSS) observations from the UAS and GCPs that have an average accuracy of one to two centimeters, while the other was generated using PPK trajectory solution for the UAS’s trajectory with two software: open-source Emlid Studio and the widely used Inertial Explorer. The use of PPK techniques in UAS mapping may have several potential benefits over traditional methods. By correcting the errors in the UAS's trajectory, a user may only need to depend on fewer ground control points, which can reduce the time and cost associated with fieldwork. This is particularly useful in areas that are difficult to access or have limited ground control point options, such as in urban or forested areas. To evaluate performance, a GNSS receiver is used to obtain measurements on checkpoints, which are used to assess the accuracies of the point clouds. In our experiments, the accuracy of the point clouds generated using PPK trajectory solution with high accuracy GCPs was found to be higher than those generated with low accuracy GNSS observations while aided with high accuracy ground control points. While the use of PPK with GCPs is generally expected to provide more accurate and reliable data than low-accuracy GNSS observations even after adjusting with GCPs, the number and distribution of GCPs can still significantly impact overall accuracy. Therefore, careful consideration of the number of GCPs and their placement is essential to achieve the desired level of efficiency and effectiveness in UAS mapping.

Using spatiotemporal stacks for precise vehicle tracking from roadside 3D LiDAR data

This paper presents the development of the JKU-ITS Last Mile Delivery Robot. The proposed approach utilizes a combination of one 3D LIDAR, RGB-D camera, IMU and GPS sensor on top of a mobile robot slope mower. An embedded computer, running ROS1, is utilized to process the sensor data streams to enable 2D and 3D Simultaneous Localization and Mapping, 2D localization and object detection using a convolutional neural network.KeywordsLast mile deliveryMobile robotSensorsSensor-fusionROS1

This paper documents a large scale, long-term autonomy dataset for robotics research collected on the University of Michigan’s North Campus. The dataset consists of omnidirectional imagery, 3D lidar, planar lidar, GPS, and proprioceptive sensors for odometry collected using a Segway robot. The dataset was collected to facilitate research focusing on long-term autonomous operation in changing environments. The dataset is composed of 27 sessions spaced approximately biweekly over the course of 15 months. The sessions repeatedly explore the campus, both indoors and outdoors, on varying trajectories, and at different times of the day across all four seasons. This allows the dataset to capture many challenging elements including: moving obstacles (e.g. pedestrians, bicyclists and cars), changing lighting, varying viewpoint, seasonal and weather changes (e.g. falling leaves and snow), and long-term structural changes caused by construction projects. To further facilitate research, we also provide ground-truth pose for all sessions in a single frame of reference.

In cryogenic regions, robots need various sensors to autonomously drive and perform their missions. These sensors should not only provide data for recognizing the environment, avoiding obstacles, and detecting their location, but should also be able to operate in a cryogenic environment. In this paper, we propose a multimodal sensor module that can acquire RGB, depth, and thermal images for use in a cryogenic environment (e.g. the Antarctic). The proposed module can explore crevasses while autonomously driving in Antarctica together with 3D Lidar and GPS sensors.

Non-Fixed Supported Transport of Large Objects by Autonomous Cooperative Control of Omni-Directional Mobile Robots Based on 3D LiDAR and Force Sensor

LSMCL: Long-term Static Mapping and Cloning Localization for autonomous robot navigation using 3D LiDAR in dynamic environments

Continuous point cloud data is being used more and more widely in practical applications such as mapping, localization and object detection in autonomous driving systems, but due to the huge volume of data involved, sharing and storing this data is currently expensive and difficult. One possible solution is the development of more efficient methods of compressing the data. Other researchers have proposed converting 3D point cloud data into 2D images, or using tree structures to store the data. In a previous study targeting streaming point cloud data, we proposed an MPEG-like compression method which utilizes simultaneous localization and mapping (SLAM) results to simulate LiDAR's operating process. In this paper, instead of imitating MPEG, we propose new strategy for more efficient reference frame distribution and more natural frame prediction, and use a different algorithm to encode the residual, greatly improving the algorithm's performance and its stability in different scenarios. We also discuss how various parameters affect compression performance. Using our proposed method, streaming point cloud data collected by LiDAR sensors can be compressed to 1/50th of its original size, with only 2 cm of Root Mean Square Error for each detected point. We evaluate our proposed method by comparing its performance with several other existing point cloud compression methods in three different driving scenarios, demonstrating that our proposed method outperforms them.